Galaxy morphology classifications based on machine learning are a typical technique to handle enormous amounts of astronomical observation data, but the key challenge is how to provide enough training data for the machine learning models. Therefore this article proposes an image data augmentation method that combines few-shot learning and generative adversarial networks. The Galaxy10 DECaLs data set is selected for the experiments with consistency, variance, and augmentation effects being evaluated. Three popular networks, including AlexNet, VGG, and ResNet, are used as examples to study the effectiveness of different augmentation methods on galaxy morphology classifications. Experiment results show that the proposed method can generate galaxy images and can be used for expanding the classification model's training set. According to comparative studies, the best enhancement effect on model performance is obtained by generating a data set that is 0.5–1 time larger than the original data set. Meanwhile, different augmentation strategies have considerably varied effects on different types of galaxies. FSL-GAN achieved the best classification performance on the ResNet network for In-between Round Smooth Galaxies and Unbarred Loose Spiral Galaxies, with F1 Scores of 89.54% and 63.18%, respectively. Experimental comparison reveals that various data augmentation techniques have varied effects on different categories of galaxy morphology and machine learning models. Finally, the best augmentation strategies for each galaxy category are suggested.

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